Apache Spark how to append new column from list/array to Spark dataframe

I am using Apache Spark 2.0 Dataframe/Dataset API I want to add a new column to my dataframe from List of values. My list has same number of values like given dataframe.

val list = List(4,5,10,7,2)
val df   = List("a","b","c","d","e").toDF("row1")

I would like to do something like:

val appendedDF = df.withColumn("row2",somefunc(list))
df.show()
// +----+------+
// |row1 |row2 |
// +----+------+
// |a    |4    |
// |b    |5    |
// |c    |10   |
// |d    |7    |
// |e    |2    |
// +----+------+

For any ideas I would be greatful, my dataframe in reality contains more columns.

Solution 1:

You could do it like this:

import org.apache.spark.sql.Row
import org.apache.spark.sql.types._    

// create rdd from the list
val rdd = sc.parallelize(List(4,5,10,7,2))
// rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[31] at parallelize at <console>:28

// zip the data frame with rdd
val rdd_new = df.rdd.zip(rdd).map(r => Row.fromSeq(r._1.toSeq ++ Seq(r._2)))
// rdd_new: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[33] at map at <console>:32

// create a new data frame from the rdd_new with modified schema
spark.createDataFrame(rdd_new, df.schema.add("new_col", IntegerType)).show
+----+-------+
|row1|new_col|
+----+-------+
|   a|      4|
|   b|      5|
|   c|     10|
|   d|      7|
|   e|      2|
+----+-------+

Solution 2:

Adding for completeness: the fact that the input list (which exists in driver memory) has the same size as the DataFrame suggests that this is a small DataFrame to begin with - so you might consider collect()-ing it, zipping with list, and converting back into a DataFrame if needed:

df.collect()
  .map(_.getAs[String]("row1"))
  .zip(list).toList
  .toDF("row1", "row2")

That won't be faster, but if the data is really small it might be negligible and the code is (arguably) clearer.